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SATELLITE PERSPECTIVE OF 2019’S FOREST FIRE DISASTER IN INDONESIA

THE AGENCY FOR METEOROLOGY, CLIMATOLOGY, AND GEOPHYSICS

OF THE REPUBLIC OF INDONESIA

Andersen Panjaitan, Tyas Tri Pujiastuti, Hanif, Mentari Ika D

AOMSUC-10

Melbourne, December 6th 2019

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NATIONAL LAND/FOREST FIRE DISASTER OF 2019

Based on Terra/Aqua MODIS, in the same period (January-September) number of hotspot in 2019 increases compare to the number of hotspot in 2018, but the hotspot in 2019 decreases about 45 % compare to number of hotspot in 2015. By the end of September 2019 a total of 857,756 hectares (2.12 million acres) had been burned. That is more than the 529,267 hectares that burned in 2018

2015 2016 2017 2018 2019

Jumlah 40,727 3,488 1,708 7,046 22,389

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ANNUAL CYCLE OF RAINFALL OVER HIGH RISK AREA VS 2015-2019 HOTSPOT

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HOTSPOT VS RAINFALL (JULY – SEPT 2019)

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BMKG SATELLITE PRODUCTS FOR FOREST FIRE DISASTER MITIGATION

Observe and monitor hotspot and land-forest fires using polar satellites data. Update every 1 hour

POLAR HOTSPOT

Provide smoke dispersion information based on RGB, Manual careful analysis every hour.

SMOKE RGB

To see the history of hotspot in each province.

10-DAYS OUTLOOK

Summary of high level confidence Hotspot, provide hotspot number in each district.

HOTSPOT DATA PER DISTRICT

Early indication of forest fire initiation, higher chance of fire during long dry period.

CONCECUTIVE DRY DAYS

Observe and monitor hotspot and land-forest fires using Himawari-8 data. Generated every 10 minutes

GEOHOTSPOT

KLHK

BPBD

TNI

POLRI

Web Based for Public

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CONSECUTIVE DRY DAYS

Dry days length based on last 120 days GSMaP (Global Satellite Mapping of Precipitation) data, enable users to determine drought potential. Dry day length represented on certain color ranges.

Monthly Rainfall Forecast

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LAND/FOREST FIRE RISK MAP

Land/forest fire risk map :

➢ Constructed using weighting function of CDD, fire-prone data, and ECMWF daily rainfallforecast

➢ Display 4 categories of risk: Low (green), medium (yellow), high (brown), very high (red)

Riau, March 19 2019 Riau, February 14 2019

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HOTSPOT DETECTION USING POLAR SATELLITES

➢ Hotspot data generated from Terra, Aqua, S-NPP,NOAA-20

➢ Appropriate resource to depict fire location, and itspattern based on long term data.

➢ Sensing temperature anomaly over 1 km2 squarearea.

Drawbacks of polar detection:

• Unable to detect hotspot under cloud covered area.

• Higher chance of omission due to Less observationfrequency

• Inevitable blank area from polar orbit satellites

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HOTSPOT DETECTION FROM HIMAWARI-8

Comparison of MODIS – AHI: 40 – 60% similarity

Aqua (MODIS)

Himawari-8 (AHI)

Drawbacks :- Less observation frequency- Included blank zoneBenefit :- Higher accuration

Drawback :- Lower resolution (2km) merely capable

for widespread fire.Benefits :- 10-min temporal resolution- Minimize chance of cloud cover hitch- Also facile for haze detection

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NEAR REAL TIME WILDFIRE RECOGNITION

GeoHotspot is an improved detection of land-forest fire based on Himawari-8 data. Suspected fire

spot (red dots) generated every 10 minutes in a daytime, enable users to response timely. There

also feasibility to evaluate the pattern using long term data.

Case: 18 March 2019

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SMOKE DISPERSION INFORMATION

Overlay Smoke RGB + Geohotspot + wind 1000 mb (GSM Model) provided hourly

Visual analysis including haze dispersion and wind direction.

Drawbacks:

• Unable to determine plume height and concentration, Only available during daytime, Require

manual analysis, Low spatial resolution

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SMOKE RGB ADJUSTMENT

Himawari-8 RGB adjustment provide more distinct color on thick smoke detection

Red : VS (Max :0.15, Max : 1, Gamma : 3)

Green : N1(Max :0.15, Max : 1, Gamma : 2)

Blue : N3 (Max :0.05, Max : 0.5, Gamma : 2)

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CONCLUSIONS

• During 2019, there are several disasters in Indonesia including forestfire, that frequently occurred every year, has been the second mostdevastating hazard this year.

• Forest fire highly correlated to rainfall pattern, thus consecutive drydays is an effective tool to estimate forest burning initiation.

• Historical data derived from Terra/Aqua shows 2019’s fire is the secondhighest in last 6 years. This confirmed with burned area gathered fromground surveys.

• BMKG keeps enhancing its ability to detect and monitor hotspots andsmoke haze. We continue to develop methods of preventing andmitigating the transboundary smoke haze issue. Adjusment on smokeRGB provide more accurate information for thick smoke dispersion.

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Thank you